IDEAS home Printed from
   My bibliography  Save this article

Semiparametric Double Balancing Score Estimation for Incomplete Data With Ignorable Missingness


  • Zonghui Hu
  • Dean A. Follmann
  • Jing Qin


When estimating the marginal mean response with missing observations, a critical issue is robustness to model misspecification. In this article, we propose a semiparametric estimation method with extended double robustness that attains the optimal efficiency under less stringent requirement for model specifications than the doubly robust estimators. In this semiparametric estimation, covariate information is collapsed into a two-dimensional score S , with one dimension for (i) the pattern of missingness and the other for (ii) the pattern of response, both estimated from some working parametric models. The mean response E ( Y ) is then estimated by the sample mean of E ( Y ∣ S ), which is estimated via nonparametric regression. The semiparametric estimator is consistent if either the “core” of (i) or the “core” of (ii) is captured by S , and attains the optimal efficiency if both are captured by S . As the “cores” can be obtained without correctly specifying the full parametric models for (i) or (ii), the proposed estimator can be more robust than other doubly robust estimators. As S contains the propensity score as one component, the proposed estimator avoids the use and the shortcomings of inverse propensity weighting. This semiparametric estimator is most appealing for high-dimensional covariates, where fully correct model specification is challenging and nonparametric estimation is not feasible due to the problem of dimensionality. Numerical performance is investigated by simulation studies.

Suggested Citation

  • Zonghui Hu & Dean A. Follmann & Jing Qin, 2012. "Semiparametric Double Balancing Score Estimation for Incomplete Data With Ignorable Missingness," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 247-257, March.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:497:p:247-257
    DOI: 10.1080/01621459.2012.656009

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlasa:v:107:y:2012:i:497:p:247-257. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.